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OMGT 5653/GNEG 590V - Introduction to Analytics 
 
Instructor:   Christopher M. Smith, Ph.D. 
 
Cellphone:   254-681-0787 (Eastern time)  
Email:  cms058@uark.edu 
Schedule:   Fall (8w1) 2019 Session – August 26th – October 15th, 2019 
Instructor Response Times 
The best way to reach me is by email.  I will respond to emails/questions within 24-36 
hours.  If you need to reach me immediately (such as during an exam), by all means please 
give me a call. 
I try to grade assessments as soon as possible.   Grades should be returned within 3 – 5 
days after the due date, or before the next similar assignment. 
 
Course Description 
Introduces data science and data analytics.  Provides basic skill instruction in the statistical 
data analysis programming language R.  Provides experience building and interpreting 
descriptive and predictive data analytics models.  Provides practice communicating those 
results to senior stakeholders and decision makers. 
Prerequisites: OMGT 4853 and OMGT 5003. 
 
Course Goals/Objectives 
The goal of this course is to introduce students to data analytics.  You will be programming 
in R.  Upon completion of this course, students should be able to: 
1. Demonstrate basic proficiency in the R programming language for statistical 
analysis.  
2. Apply Affinity Analysis to analyze data to support decision-making.   
3. Apply descriptive statistical and graphical displays of data to communicate results 
of data analytics to senior stakeholders and decision makers.   
4. Apply classification methods such as K-Nearest Neighbors and Classification 
Trees to evaluate solutions to complex engineering problems.  
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5. Use Linear Regression to make statistical predictions to support decision-making.  
6. Use the data reduction method of K-Means Analysis to select between solutions to 
complex engineering problems. 
7. Develop executive summaries, oral presentations, and detailed technical reports 
to communicate results of data analytics to senior stakeholders and decision 
makers. 
 
Required Materials  
 
Textbook 
 Data Analytics with R, Second Edition. Viswa Viswanathan, 
Infivista Inc; 2nd edition (August 29, 2015) ISBN-13: 978-
1941773024.   
Correct textbook must be ordered and in hand by the first 
day of class.  Utilizing expedited shipping option may be 
required.  Ensure you order the textbook with the correct 
ISBN. International or Flexible textbooks are not supported 
by the instructor. Failure to order the correct textbook in a 
timely manner will adversely affect your success and your grade in class. 
Software  
• The R programming language, as executed in RStudio in the University of Arkansas 
Virtual Lab (login and software access provided free with course registration.) 
• Microsoft Excel 2013 or later (freely available to you with your enrollment at the 
University, from https://techarticles.uark.edu/microsoft/office/).   Also available in 
the Virtual Lab, free with your course registration.) 
• Word processing and presentation software that saves files in Microsoft Office 
formats such as:  
o Microsoft Word and Microsoft PowerPoint (also available in MS Office suites)  
• Latest version of Java to use required applications 
 
Note: R and RStudio are freely available and students are welcome to download and install 
them on their individual computers for convenience.  However, we cannot provide 
technical support for individual installations.  We can only help you with the R code you 
run in our Virtual Lab. 
Check the UA Computer Store for student discounts on software. 
 
 
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Activities and Assignments 
Description Total Points Percent of 
Grade 
7 Individual Activity Packs @ 25 points 
apiece.  An Individual Activity Pack is 
one or more homework assignments in 
a bundle. 
175 17.5% 
6 Team Activities @ 25 points apiece 150 15.0% 
Exam 1 (Weeks 1-4, given ~ Week 4) 300 30.0% 
Exam 2 (Weeks 5-8, given ~ Week 8) 375 37.5% 
TOTAL 1000 100.0% 
 
Grading 
 
✓ A = 90-100%  
✓ B = 80-89%   
✓ C = 70-79%   
✓ D = 60-69%   
✓ F = < 60%     
Grades of “I” are awarded for emergency situations ONLY as identified by the University 
Handbook. Hard copy documentation must be provided in such instances. Incomplete 
grades automatically turn into an “F” after a certain date. Consult the registrar’s office for 
more information. 
  
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Course Outline 
 
Week Dates Subject Course 
Goals and 
Objectives 
Assignments 
1 8/26 – 9/1  Introduction to R 1 Week 1 Individual Activity 
Pack (IAP)  
Week 1 Team Assignment 
2 9/2 – 9/8 Affinity Analysis  2,7 Week 2 IAP 
Week 2 Team Assignment 
3 9/9 – 9/15 Descriptive and 
Graphical Displays 
3,7 Week 3 IAP 
Week 3 Team Assignment 
4 9/16 – 9/22 Classification Methods:  
K-Nearest Neighbors 
(KNN) 
4,7 IAP 4 
(There is no Week 4 Team 
Assignment) 
 
Midterm Exam 
5 9/23 – 9/29 Classification Methods:  
Trees  
4,7 Week 5 IAP 
Week 5 Team Assignment 
6 9/30 – 10/6 Linear Regression 5,7 Week 6 IAP 
Week 6 Team Assignment 
7/8 10/7 – 10/13 Data Reduction:  K-
Means Clustering 
6,7 Week 7 IAP 
Week 7 Team Assignment 
7/8 10/7 – 10/15 Communicate results to 
stakeholders and 
management 
 
Note there is overlap 
between Week 7 and 
Week 8. 
7 Final Exam 
 
 
  
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Policies 
 
Late Work Policy 
Individual and Team Activities —if submitted late, the assignment score will include a late 
assignment deduction of 5 points for each day or part of a day the assignment is submitted 
past the due date/time.  Please see end of term policy below. 
Exams—no exams accepted after the exam due date.   
 
Attendance Policy 
 
This is an asynchronous online course, which means there are no specific attendance 
hours, but you should budget approximately 10 hours per week to this course.  You can 
structure your participation around your work and family obligations.  Students are 
expected to submit weekly homework, participate fully in each weekly Team Activity, 
and take each of the two exams within the time window. 
 
If you need to make up work due to unforeseen absences, please contact the professor. 
 
Academic Honesty 
 
I am committed to the principle of academic honesty, and I expect each student in my class 
to maintain a high standard of academic integrity. My commitment to you, the student, is to 
provide a learning environment that promotes academic honesty in and out of the 
classroom.  
"As a core part of its mission, the University of Arkansas provides students with the 
opportunity to further their educational goals through programs of study and research in 
an environment that promotes freedom of inquiry and academic responsibility. 
Accomplishing this mission is only possible when intellectual honesty and individual 
integrity prevail. Each University of Arkansas student is required to be familiar with and 
abide by the University’s ‘Academic Integrity Policy' at honesty.uark.edu. Students with 
questions about how these policies apply to a particular course or assignment should 
immediately contact their instructor." 
Plagiarism is often misunderstood.  It can be defined as submitting someone else’s work as 
your own.  It is not permissible to “cut and paste” and then just cite another’s work.  In 
writing for homework or projects, you should read and learn, process through your mind, 
relate ideas, and then express what you learned in your own words.  Cite the references 
where you found your information.  If you do use someone else’s words, you must use 
quotation marks and cite.  You should not overuse quotes – save them for a rare 
occurrence. 
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Academic Appeals 
Academic appeals: Students are first encouraged to resolve academic conflicts and 
complaints informally with the instructor involved, through their department, or through 
the assistance of the University Ombuds Office, which can provide objective and 
confidential mediation. To assist students in identifying the appropriate contact person, 
please view this List of Program, Department, and College Contacts. A flow chart is also 
available for viewing. If an informal resolution cannot be reached, there are procedures for 
students to pursue with complaints of an academic nature. Refer to either the 
Undergraduate Catalog of Studies or the Graduate Catalog of Studies for appeals structures 
and formal procedures for academic grievances.  
Computer Access Policy 
This course is offered as an online course and it is assumed that you have the minimum 
system requirements to participate (see the START HERE section of the course). It is your 
responsibility to ensure that you can access all course materials, participate in discussions 
and upload or download materials and software used for this course. In addition, care has 
been taken to ensure that the software that is used for this course does not require any out 
of the ordinary system set-ups. But, if your system does not meet the minimum 
requirements then it is your responsibility to maintain your system to meet the 
requirements so that you may participate in this course. Technical difficulties on your part 
will not excuse you from the timely completion of assignments. If you do experience 
technical difficulties, please make sure that you contact me immediately so that proper 
assistance might be provided. 
Netiquette 
Netiquette is a set of rules for behaving properly online. It is important that all participants 
in online courses be aware of proper online behavior and respect each other. 
Use appropriate language for an educational environment: 
• Use complete sentences.  
• Use proper spelling and grammar.  
• Avoid idioms and slang.  
• Do not use obscene or threatening language.  
Remember that the university values diversity and encourages discourse. Be respectful of 
differences while engaging in online discussions. For more information about Netiquette, 
see The Core Rules for Netiquette by Virginia Shea. 
CAPS 
Academic problems are often related to the non-academic events in your lives. You are 
welcome to visit with the capable staff at the UA Counseling and Psychological Services 
(with offices in the North Quadrangle). You can telephone them at 479-575-CAPS. The fact 
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that you telephone is also entirely confidential. Each semester they conduct a variety of 
support groups dealing with stressful issues. 
Accommodations under the Americans with Disabilities Act 
University of Arkansas Academic Policy Series 1520.10 requires that students with 
disabilities are provided reasonable accommodations to ensure their equal access to course 
content. If you have a documented disability and require accommodations, please contact 
me privately at the beginning of the semester to make arrangements for necessary 
classroom adjustments. Please note, you must first verify your eligibility for these through 
the Center for Educational Access (contact 479–575–3104 or visit cea.uark.edu for more 
information on registration procedures). 
Equal Treatment for All 
The UA "Catalog of Studies" reports that the Campus Council supports equal treatment for 
all. It "does not condone discriminatory treatment of students or staff on the basis of age, 
disability, ethnic origin, marital status, race, religious commitment, sex, or sexual 
orientation in any of the activities conducted on this campus. Members of the faculty are 
requested to be sensitive to this issue when, for example, presenting lecture material, when 
assigning seating within the classroom, when selecting groups for laboratory experiments, 
and when assigning student work. The University faculty, administration, and staff are 
committed to provide an equal educational opportunity to all students." 
 
Our class work will conform to the principle of equal treatment.  
Inclement Weather or Technical Problems 
Weather is unlikely to force cancellation of any online classes or activities.  If a known 
weather event is approaching, it is good practice for students to turn in work early in case 
of local power outages 
 
Caveat re: changes to syllabus  
The above schedule and procedures in this course are subject to change at the discretion 
of the instructor. 
 
Revised 4/29/2019